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Computer Vision and Machine Intelligence for Renewable Energy Systems
Computer Vision and Machine Intelligence in Renewable Energy Systems, the first release in Elsevier's cutting-edge new series, Advances in Intelligent Energy Systems, offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration. The book equips readers with a variety of essential tools and applications, outlining the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence and breaking down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures.Other sections offer case studies and applications to a wide range of renewable energy source and the future possibilities of the technology. This book provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids.
R 6,777.00
Machine Learning and Computer Vision for Renewable Energy
As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems. Machine Learning and Computer Vision for Renewable Energy positions itself as a catalyst for this change. The book not only addresses the immediate concerns of the energy sector but also details how to achieve a more sustainable future. By emphasizing breakthroughs in CV and AI, the objective is clear: to drive societal progress through research, innovation, and technological advancements in the domain of renewable energy. Academic researchers, professors, college students, and business professionals focused on the intersection of digital transformation and renewable energy will find this book to be an indispensable guide to navigating the challenges and opportunities that lie ahead. With a diverse array of recommended topics, this book stands as a testament to the evolving landscape of AI and computer vision, shaping a sustainable energy future for generations to come.
R 8,696.60
Advanced Computational Techniques for Renewable Energy Systems
In this book, one hundred selected articles, in which the technology and science elite share, contribute to technology development, collaborate and evolve the latest cutting-edge technologies, open ecosystem resources, new innovative computing solutions, hands-on labs and tutorials, networking and community building, to ensure better integration of artificial intelligence into renewable energy systems. Innovation in computing continues at a growing pace. The key to success in this area is not only hardware, but also the ability to leverage rapid advances in artificial intelligence (including machine learning and deep learning), data analytics, data streaming, and cloud computing, which go hand in hand with intensive research activity on the underlying computational methods. The chapters in this book are organized into thematic sections on: advanced computing techniques; artificial intelligence; smart and sustainable cities; renewable energy systems; materials in renewable energy; smart energy efficiency; smart cities applications: recent developments and new trends; online, supervision of renewable energy platforms; predictive control in renewable systems; smart embedded systems for photovoltaic applications.
R 9,679.01
Smart Computing and Control Renewable Energy Systems
This essential book bridges the gap between cutting-edge artificial intelligence and the dynamic world of renewable energy systems. Embark on a journey to the forefront of sustainable energy innovation with this groundbreaking collection of research papers and expert insights.Designed for curious minds and industry leaders alike, this comprehensive resource offers:- A deep dive into the latest advancements in smart computing for sustainable energy.- Exploration of AI-driven techniques revolutionizing energy efficiency and management.- Real-world applications showcasing the transformative power of intelligent systems in renewables.- Insights into futuristic energy infrastructures powered by artificial intelligence.- A perfect blend of theoretical foundations and practical implementations.To a seasoned researcher pushing the boundaries of knowledge, a graduate student aspiring to make a mark, or an industry professional staying ahead of the curve, this book is a gateway to the future of energy.Discover how machine learning is reshaping solar forecasting, uncover the potential of autonomous systems in energy storage, and explore the role of AI in crafting smarter, more sustainable cities. From predictive maintenance that ensures uninterrupted power to intelligent control systems optimizing energy generation, this book covers it all.Don't just witness the renewable energy revolution—be part of it. This book equips readers with the knowledge and inspiration to drive innovation in this critical field. It is more than a collection of papers; it is a roadmap to a sustainable future where smart computing and renewable energy converge.Prepare to challenge your assumptions, expand your expertise, and contribute to a greener tomorrow. Order your copy today and position yourself at the vanguard of the smart energy movement!
R 8,059.00
Artificial Intelligence and Machine Learning in Heat Transfer Optimization for Sustainable Energy Systems
Artificial Intelligence and Machine Learning in Heat Transfer Optimization for Sustainable Energy Systems examines how to use AI/ML-driven methodologies to enhance heat transfer processes in energy systems, such as industrial heat recovery, HVAC systems, and renewable energy generation, with a focus on sustainability.Exploring applications in sustainable energy systems, renewable resources, and smart grids, the book presents intelligent control methodologies, predictive modeling, real-time data analysis, and thermal management with deep learning. It covers AI-driven heat transfer monitoring, which is critical for a variety of applications beyond sustainability, including industrial production, aerospace, automotive, and electronics cooling. The chapters feature numerous case studies of AI/ML implementation in heat exchangers, power plants, and renewable energy systems.This book will interest researchers and graduate students studying the intersection of AI, ML, and heat transfer optimization as applied to energy systems.
R 7,635.81
Physics-Aware Machine Learning for Integrated Energy Systems Management
Physics-Aware Machine Learning for Integrated Energy Systems Management, a new release in the Advances in Intelligent Energy Systems series, guides the reader through this state-of-the-art approach to computational methods, from data input and training to application opportunities in integrated energy systems. The book begins by establishing the principles, design, and needs of integrated energy systems in the modern sustainable grid before moving into assessing aspects such as sustainability, energy storage, and physical-economic models. Detailed, step-by-step procedures for utilizing a variety of physics-aware machine learning models are provided, including reinforcement learning, feature learning, and neural networks.Supporting students, researchers, and industry engineers to make renewable-integrated grids a reality, this book is a holistic introduction to an exciting new approach in energy systems management.
R 5,444.00
AI Control Strategies for Optimal Power Distribution
The global energy landscape is transforming as economies prioritize decarbonization and energy resilience. Microgrids—intelligent, autonomous energy systems integrating renewable sources, energy storage, and advanced controls—are central to this revolution, offering a promising pathway to sustainable and secure energy. Their potential to revolutionize the sector is both promising and inspiring, motivating further exploration of their capabilities. However, microgrid operations present significant challenges because traditional rule-based approaches often struggle to accommodate uncertainties inherent in renewable energy systems and fluctuating load demandsAI Control Strategies for Optimal Power Distribution explores the intersection of artificial intelligence and renewable energy systems, with microgrids as its central focus. These small-scale power systems combine renewable sources like solar and wind with energy storage, offering cleaner, more reliable power for homes, businesses, and communities. The book provides a comprehensive overview of AI-driven control strategies for optimal power distribution in microgrids. It explores AI's theoretical foundations, applications in power systems, and the specific challenges and opportunities within microgrid contexts. The book illustrates distinct advancements in the microgrid space achieved through AI's transformative power. As AI transitions from predictive to generative capabilities, it facilitates multiple aspects of establishing and sustaining highly efficient, versatile microgrids. Explaining how combining AI and microgrids is game-changing, the book covers such AI applications as forecasting, optimization, and trading.
R 6,594.00
Enabling Methodologies for Renewable and Sustainable Energy
This book aims to provide practical aspects of, and an introduction to, the applications of various technological advancement tools, such as AI, machine learning to design, big data, cloud computing, and IoT, to model, characterize, optimize, forecast, and do performance prediction of renewable energy exploitation. It further discusses new avenues for energy sources such as hydrogen energy generation and energy storage technologies including existing policies and case studies for a better understanding of renewable energy generation.Features:Covers technologies considered to explore, predict, and perform operation and maintenance of renewable energy sourcesAids in the design and use of renewable energy sources, including the application of artificial intelligence in a real-time environmentIncludes IoT, cloud computing, big data, smart grid, and different optimization techniques for resource forecasting, installation, operation, and optimization of energyDiscusses the principle of integration/hybridization of renewable energy sources along with their optimization based on energy requirementsReviews the concepts and challenges involved in the implementation of smart gridsThis book is aimed at researchers and graduate students in renewable energy engineering, computer and mechanical engineering, novel technologies, and intelligent systems.
R 2,792.62
Applied Soft Computing and Embedded System Applications in Solar Energy
Applied Soft Computing and Embedded System Applications in Solar Energy deals with energy systems and soft computing methods from a wide range of approaches and application perspectives. The authors examine how embedded system applications can deal with the smart monitoring and controlling of stand-alone and grid-connected solar photovoltaic (PV) systems for increased efficiency. Growth in the area of artificial intelligence with embedded system applications has led to a new era in computing, impacting almost all fields of science and engineering. Soft computing methods implemented to energy-related problems regularly face data-driven issues such as problems of optimization, classification, clustering, or prediction. The authors offer real-time implementation of soft computing and embedded system in the area of solar energy to address the issues with microgrid and smart grid projects (both renewable and non-renewable generations), energy management, and power regulation. They also discuss and examine alternative solutions for energy capacity assessment, energy efficiency systems design, as well as other specific smart grid energy system applications. The book is intended for students, professionals, and researchers in electrical and computer engineering fields, working on renewable energy resources, microgrids, and smart grid projects. Examines the integration of hardware with stand-alone PV panels and real-time monitoring of factors affecting the efficiency of the PV panels Offers real-time implementation of soft computing and embedded system in the area of solar energy Discusses how soft computing plays a huge role in the prediction of efficiency of stand-alone and grid-connected solar PV systems Discusses how embedded system applications with smart monitoring can control and enhance the efficiency of stand-alone and grid-connected solar PV systems Explores swarm intelligence techniques for solar PV parameter estimationDr. Rupendra Kumar Pachauri is Assistant Professor – Selection Grade in the Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies (UPES), Dehradun, India. Dr. Jitendra Kumar Pandey is Professor & Head of R&D in the University of Petroleum and Energy Studies (UPES), Dehradun, India. Mr. Abhishek Sharma is working as a research scientist in the research and development department (UPES, India). Dr. Om Prakash Nautiyal is working as a scientist in Uttarakhand Science Education & Research Centre (USERC), Department of Information and Science Technology, Govt. of Uttarakhand, Dehradun, India.Prof. Mangey Ram is working as a Research Professor at Graphic Era Deemed to be University, Dehradun, India.
R 2,991.19
Artificial Intelligence Empowered Smart Energy Systems
An illuminating and up-to-date exploration of the latest advances in AI-empowered smart energy systems In Artificial Intelligence Empowered Smart Energy Systems, the editors along with a team of distinguished researchers deliver an original and comprehensive discussion of artificial intelligence enabled smart energy systems. The book offers a deep dive into AI's integration with energy, examining critical topics like renewable energy forecasting, load monitoring, fault diagnosis, resilience-oriented optimization, and efficiency-driven control. The contributors discuss the real-world applications of AI in smart energy systems, showing you AI's transformative effects on energy landscapes. It provides practical solutions and strategies to address complicated problems in energy systems. The book also includes: A thorough introduction to cybersecurity, privacy, and virtual power plantsComprehensive demonstrations of the effective leveraging of AI technologies in energy systemsPractical discussions of the potential of AI to create sustainable, efficient, and resilient energy systemsDetailed case studies and real-world examples of AI's implementation in smart energy systems Perfect for researchers, data scientists, and policymakers, Artificial Intelligence Empowered Smart Energy Systems will also benefit graduate and senior undergraduate students in both the tech and energy industries.
R 4,559.00
Next-Generation Smart Systems
This compilation of papers captures the essence of the International Conference on Smart Systems for Sustainable Development using Advanced Computational Techniques (ICSSA 2025). It provides a comprehensive exploration of how next-generation computing drives ecological and social resilience and highlights the pivotal role of intelligent automation and data-driven strategies in solving complex global challenges.It delves into the integration of Artificial Intelligence (AI), Internet of Things (IoT), and high-performance computing to create "green" technological ecosystems. This book covers a broad spectrum of research, including smart grid management, precision agriculture, carbon footprint optimization through machine learning, and the development of circular economy models via blockchain. By synthesizing theoretical frameworks with real-world case studies, the contributions illustrate how advanced algorithms can minimize resource waste while maximizing industrial efficiency. Together, these papers offer a roadmap for sustainable digital transformation across energy, healthcare, and urban planning sectors by emphasizing the shift toward "Cognitive Sustainability", where systems do not just automate tasks but actively learn to balance human needs with environmental constraints.This book is an essential resource for academic researchers, post-graduate students, and industry practitioners specializing in computer science and environmental engineering. It also serves policymakers and urban planners seeking data-backed strategies to implement sustainable technologies within smart city infrastructures and corporate ESG frameworks.
R 2,454.00
Renewable Energy Transition with Artificial Intelligence
Explores harnessing AI to overcome strategic and operational challenges in renewable energy transition The urgent need to decarbonize global energy systems has propelled renewable energy into a position of unprecedented importance, yet this shift presents major technical, economic, and policy challenges. Increasing reliance on intermittent energy sources such as solar and wind demands more effective forecasting, grid coordination, and flexibility. Artificial Intelligence (AI) offers powerful tools to meet these challenges by learning from data, modeling complex interactions, and enabling real-time optimization across generation, transmission, and consumption. Renewable Energy Transition with Artificial Intelligence: Challenge-driven Solutions highlights successful pathways of knowledge transfer between academia and industry through case studies drawn from wind, solar, and emerging energy sources. Focusing on challenge-driven problem solving, the authors showcase transferable strategies that overcome pressing obstacles such as the lack of open datasets, the reluctance to adopt opaque predictive models, and insufficient performance benchmarks. Contributions by leading experts emphasize explainable AI, collaborative innovation, and the vital role of shared infrastructures for data and knowledge exchange. The book also draws from the authors’ international workshop with diverse stakeholders, underscoring the importance of cross-sector cooperation in ensuring sustainable and scalable impact. Adopting a challenge-driven framework linking AI innovation with renewable energy adoption, this title: Integrates perspectives from academia, industry, and the public sector to identify scalable solutionsDemonstrates methods for bridging the “black box” problem in neural network–based energy forecastingAddresses data scarcity by proposing solutions for open access, standardization, and benchmarking in renewables AIProvides practical insights for distributed generation, storage, and demand-response managementExplores future directions for explainable AI in energy system integration and resilience Both a roadmap and a reference point for integrating AI into renewable systems to accelerate global decarbonization, this book is designed for advanced students, researchers, and practitioners in engineering, computer science, and renewable energy. It is suitable for courses such as Renewable Energy Systems, Artificial Intelligence Applications in Engineering, and Energy Policy and Technology within graduate and postgraduate degree programs in engineering, data science, and environmental studies.
R 5,373.00
Optimization Techniques for Hybrid Power Systems: Renewable Energy, Electric Vehicles, and Smart Grid
Optimization Techniques for Hybrid Power Systems: Renewable Energy, Electric Vehicles, and Smart Grid is a comprehensive guide that delves into the intricate world of renewable energy integration and its impact on electrical systems. With the current global energy crisis and the urgent need to address climate change, this book explores the latest advancements and research surrounding optimization techniques in the realm of renewable energy. This book has a focus on nature-inspired and meta-heuristic optimization methods, and it demonstrates how these techniques have revolutionized renewable energy problem-solving and their application in real-world scenarios. It examines the challenges and opportunities in achieving a larger utilization of renewable energy sources to reduce carbon emissions and air pollutants while meeting renewable portfolio standards and enhancing energy efficiency. This book is explored as a crucial recipe for modern power system processes and management. The integration of energy storage, distributed generation, demand response, and thermally activated technologies into electric distribution and transmission systems is examined in detail. The book also emphasizes the importance of smart-grid technology, artificial intelligence (AI), and advanced multi-energy systems in achieving sustainability and carbon neutrality. This book serves as a valuable resource for researchers, academicians, industry delegates, scientists, and final-year master's degree students. It covers a wide range of topics, including novel power generation technology, advanced energy conversion systems, low-carbon technology in power generation and smart grids, AI-based control strategies, data analytics, electrified transportation infrastructure, and grid-interactive building infrastructure.
R 11,224.69
IoT and Analytics in Renewable Energy Systems (Volume 2)
Smart cities emanate from a smart renewable-energy-aided power grid. The smart grid technologies offer an array of benefits like reliability, availability, and resiliency. Smart grids phenomenally contribute to facilitating cities reaching those sustainability goals over time. Digital technologies, such as the Internet of Things (IoT), automation, artificial intelligence (AI) and machine learning (ML) significantly contribute to the two-way communication between utilities and customers in smart cities. Five salient features of this book are as follows: Smart grid to the smart customer Intelligent computing for smart grid applications Novel designs of IoT systems such as smart healthcare, smart transportation, smart home, smart agriculture, smart manufacturing, smart grid, smart education, smart government, smart traffic management systems Innovations in using IoT and AI in improving resilience of smart energy infrastructure Challenges and future research directions of smart city applications
R 7,628.46
Data Analytics and Learning
This book presents new theories and working models in the areas of data analytics and learning. The papers included in this book were presented at the third International Conference on Data Analytics and Learning (DAL 2024), which was organized by MGM's College of Engineering Nanded, Maharashtra, India in association with the Department of Studies in Computer Science, University of Mysore, Mysuru, Karnataka, India. The areas covered include pattern recognition, image processing, deep learning, computer vision, data analytics, machine learning, artificial intelligence, and intelligent systems.
R 11,949.06
Proceedings of 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication
This book gathers selected papers presented at International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC 2021), held in Krishna Engineering College, Ghaziabad, India, during 10 – 11 December, 2021. This book discusses key concepts, challenges and potential solutions in connection with established and emerging topics in advanced computing, renewable energy and network communications.
R 8,686.29
R 1,856.00
Computer Vision in Control Systems-2
The research book is focused on the recent advances in computer vision methodologies and innovations in practice. The Contributions include:· Human Action Recognition: Contour-Based and Silhouette-based Approaches. · The Application of Machine Learning Techniques to Real Time Audience Analysis System. · Panorama Construction from Multi-view Cameras in Outdoor Scenes. · A New Real-Time Method of Contextual Image Description and Its Application in Robot Navigation and Intelligent Control. · Perception of Audio Visual Information for Mobile Robot Motion Control Systems. · Adaptive Surveillance Algorithms Based on the Situation Analysis.· Enhanced, Synthetic and Combined Vision Technologies for Civil Aviation.· Navigation of Autonomous Underwater Vehicles Using Acoustic and Visual Data Processing. · Efficient Denoising Algorithms for Intelligent Recognition Systems. · Image Segmentation Based on Two-dimensional Markov Chains.The book is directed to the PhD students, professors, researchers and software developers working in the areas of digital video processing and computer vision technologies.
R 4,055.48
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Machine Vision
This 2004 book is an accessible and comprehensive introduction to machine vision. It provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises that give insights into the development of practical image processing algorithms. The authors begin with a review of mathematical principles and go on to discuss key issues in image processing such as the description and characterization of images, edge detection, restoration and feature extraction, segmentation, texture and shape. They also discuss image matching, statistical pattern recognition, clustering, and syntactic pattern recognition. Important applications are described, including optical character recognition and automatic target recognition. Software and data used in the book can be found at www.cambridge.org/9780521830461. A useful reference for practitioners, the book is aimed at graduate students in electrical engineering, computer science and mathematics.
R 3,829.00
Intelligent Vision and Computing
This book includes selected papers presented at 5th International Conference on Intelligent Vision and Computing (ICIVC 2025), held at The ICFAI University, Dehradun, India, during 13–14 June 2025. The conference proceedings is a collection of high-quality research articles in the field of intelligent vision and computing. The topics covered in the book are artificial intelligence, machine learning, deep learning, internet of things, information security, embedded systems, cloud computing, quantum computing, bio-inspired intelligence, cyber-physical systems, hybrid systems, intelligence for security, data mining, evolutionary optimization, swarm intelligence, signal processing, blockchain technology, big data applications, natural language processing, data acquisition, storage and retrieval for big data, data representation, processing, imaging sensors technology, features extraction, image segmentation, deep learning, convolutional neural network, biometrics recognition, biomedical imaging, intelligent transport systems, and human–computer interaction.
R 10,257.00
Intelligent Vision and Computing
This book includes selected papers presented at 5th International Conference on Intelligent Vision and Computing (ICIVC 2025), held at The ICFAI University, Dehradun, India, during 13–14 June 2025. The conference proceedings is a collection of high-quality research articles in the field of intelligent vision and computing. The topics covered in the book are artificial intelligence, machine learning, deep learning, internet of things, information security, embedded systems, cloud computing, quantum computing, bio-inspired intelligence, cyber-physical systems, hybrid systems, intelligence for security, data mining, evolutionary optimization, swarm intelligence, signal processing, blockchain technology, big data applications, natural language processing, data acquisition, storage and retrieval for big data, data representation, processing, imaging sensors technology, features extraction, image segmentation, deep learning, convolutional neural network, biometrics recognition, biomedical imaging, intelligent transport systems, and human–computer interaction.
R 10,257.00
R 977.00
Intelligent Vision and Computing
This book includes selected papers presented at 5th International Conference on Intelligent Vision and Computing (ICIVC 2025), held at The ICFAI University, Dehradun, India, during 13–14 June 2025. The conference proceedings is a collection of high-quality research articles in the field of intelligent vision and computing. The topics covered in the book are artificial intelligence, machine learning, deep learning, internet of things, information security, embedded systems, cloud computing, quantum computing, bio-inspired intelligence, cyber-physical systems, hybrid systems, intelligence for security, data mining, evolutionary optimization, swarm intelligence, signal processing, blockchain technology, big data applications, natural language processing, data acquisition, storage and retrieval for big data, data representation, processing, imaging sensors technology, features extraction, image segmentation, deep learning, convolutional neural network, biometrics recognition, biomedical imaging, intelligent transport systems, and human–computer interaction.
R 10,257.00
Bioinspired Intelligent Systems: From Robotics and Computer Vision to Trustworthy Applications
The 93 full papers included in this book were carefully reviewed and selected from 187 submissions. They were organized in topical sections as follows:Part I: Machine Learning in Neuroscience; Functional Evaluation of Neuromotor and Cognitive Disorders; Intelligent Systems for the Study and Support of Youth Mental Health; Socio-Cognitive, Affective and Physiological Computing; Intelligent Systems for Assessment, Treatment, and Assistance in early stages of Alzheimer; Neuroprosthesis, Ethics; and European Culture and Technology Lab;Part II: Machine Learning in Computer Vision and Robotics; Robotics; Bio-Inspired Computing Approaches; Social and Civil Engineering through Human AI Translations; Explainable, Robust and Trustworthy Machine Learning and its Applications; Learning tools to lecture; and Other Applications.
R 3,552.99
R 2,357.79
Computer Vision in Control Systems-2
The research book is focused on the recent advances in computer vision methodologies and innovations in practice. The Contributions include:· Human Action Recognition: Contour-Based and Silhouette-based Approaches. · The Application of Machine Learning Techniques to Real Time Audience Analysis System. · Panorama Construction from Multi-view Cameras in Outdoor Scenes. · A New Real-Time Method of Contextual Image Description and Its Application in Robot Navigation and Intelligent Control. · Perception of Audio Visual Information for Mobile Robot Motion Control Systems. · Adaptive Surveillance Algorithms Based on the Situation Analysis.· Enhanced, Synthetic and Combined Vision Technologies for Civil Aviation.· Navigation of Autonomous Underwater Vehicles Using Acoustic and Visual Data Processing. · Efficient Denoising Algorithms for Intelligent Recognition Systems. · Image Segmentation Based on Two-dimensional Markov Chains.The book is directed to the PhD students, professors, researchers and software developers working in the areas of digital video processing and computer vision technologies.
R 3,700.16
Soft Computing Models in Industrial and Environmental Applications
This book constitutes the proceedings of the 21st International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2026, held in Marbella, Spain, during June 18-19, 2026.The 58 full papers included in this volume were carefully reviewed and selected from 121 submissions. The papers cover the following topical sections: Computer Vision Applications; Prediction and Monitoring; and Systems and Optimization. The papers also include the following special sessions: Efficiency and Explainability in Machine Learning and Soft Computing; Time Series Forecasting in Industrial and Environmental Applications; Quantum Computing; Intelligent Techniques Applied to Modelling and Control in Engineering focused on Marine Energy Systems and Robotics; Machine Learning and Computer Vision in Industry 4.0; Optimization, Modeling and Control by Soft Computing Techniques; EUropean Transnational Educational.
R 4,346.87
Energy Minimization Methods in Computer Vision and Pattern Recognition
This volume consists of the 42 papers presented at the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR2001),whichwasheldatINRIA(InstitutNationaldeRechercheen Informatique et en Automatique) in Sophia Antipolis, France, from September 3 through September 5, 2001. This workshop is the third of a series, which was started with EMMCVPR’97, held in Venice in May 1997, and continued with EMMCVR’99, which took place in York, in July 1999. Minimization problems and optimization methods permeate computer vision (CV), pattern recognition (PR), and many other ?elds of machine intelligence. The aim of the EMMCVPR workshops is to bring together people with research interests in this interdisciplinary topic. Although the subject is traditionally well represented at major international conferences on CV and PR, the EMMCVPR workshops provide a forum where researchers can report their recent work and engage in more informal discussions. We received 70 submissions from 23 countries, which were reviewed by the members of the program committee. Based on the reviews, 24 papers were - cepted for oral presentation and 18 for poster presentation. In this volume, no distinction is made between papers that were presented orally or as posters. The book is organized into ?ve sections, whose topics coincide with the ?ve s- sionsoftheworkshop:“ProbabilisticModelsandEstimation”,“ImageModelling and Synthesis”, “Clustering, Grouping, and Segmentation”, “Optimization and Graphs”, and “Shapes, Curves, Surfaces, and Templates”.
R 3,097.56
Machine Learning in Medical Imaging and Computer Vision
Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment. This edited book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision. Machine Learning in Medical Imaging and Computer Vision provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions. The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields.
R 4,417.00
Computational Intelligence, Networked Systems and Their Applications
This book constitutes the second part of the refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2014, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014, held in Shanghai, China, in September 2014. The 159 revised full papers presented in the three volumes of CCIS 461-463 were carefully reviewed and selected from 572 submissions. The papers of this volume are organized in topical sections on advanced neural network theory and algorithms; advanced evolutionary computing theory and algorithms, such as particle swarm optimization, differential evolution, ant colonies, artificial life, artificial immune systems and genetic algorithm; fuzzy, neural, and fuzzy-neuro hybrids; intelligent modeling, monitoring, and control of complex nonlinear systems; intelligent modeling and simulation of climate change; communication and control for distributed networked systems.
R 2,222.82
Intelligent Renewable Energy Systems
Focused on renewable energy systems and the development of information and communication technologies (ICTs) for their integration in smart grids, this book presents recent advances and methods that help to ensure that power generation from renewable sources remains stable, that power losses are minimized, and that the reliable functioning of these power generation units is maintained.The book highlights key topics and technologies for renewable energy systems including the intelligent control of power generators, power electronics that connect renewable power generation units to the grid, and fault diagnosis for power generators and power electronics. In particular, the following topics are addressed:• Modeling and control of power generators (PMSGs, DFIGs);• Modeling and control of power electronics (converters, inverters); • Modeling and fault diagnosis of the transmission and distribution Grid; and• Modelling and control of distributed power generation units (interconnected synchronous generators or photovoltaic units).Because of the above coverage, members of the wider engineering community will find that the nonlinear control and estimation methods presented provide essential insights into the functioning of renewable energy power systems, while the academic community will find the book a valuable textbook for undergraduate or graduate courses on renewable energy systems.
R 6,228.00
Data-Driven Energy Management and Tariff Optimization in Power Systems
Presents a comprehensive guide to transforming power systems through data Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning. Drawing on global expertise and real-world case studies, the book bridges the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book’s accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges. Integrating rigorous analysis with application-oriented strategies, this book: Presents advanced techniques in machine learning, predictive modeling, and pattern recognition tailored to energy management and tariff designProvides accessible explanations of complex algorithms through a didactic and visual teaching style, including informative tables and illustrationsHighlights tools for grid stability, demand forecasting, and peak load management using high-resolution energy dataAddresses the integration of renewable energy sources into existing infrastructures through data-driven optimization Designed for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.
R 5,210.00
Pattern Recognition and Computer Vision
This 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18-20, 2024. The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.
R 3,557.84
Advances in Computational Vision and Robotics
This book highlights the transformative impact of these technologies in shaping the future of automation and intelligent systems. By integrating computational vision and robotics into various sectors, these advancements enable smarter decision-making, seamless human-machine interactions, and enhanced efficiency in dynamic environments. Future applications include autonomous systems in transportation, intelligent surveillance, and robotic healthcare solutions, creating more responsive and adaptive systems that align with the evolving needs of society. This book provides an insight into the latest innovations and state-of-the-art research in the fields of computational vision and robotics, showcasing their potential to revolutionize industries such as manufacturing, agriculture, logistics, and disaster management. It spans a diverse array of topics, including:Computational vision and AI applications.Advanced image processing and recognition techniques.Intelligent systems and interaction.By merging cutting-edge theoretical insights with practical applications, this book provides researchers, practitioners, and students with the essential knowledge and tools to explore and advance within the dynamic field of computational vision and robotics. Computational vision and robotics highlighted the transformative potential of computational vision and robotics in revolutionizing industries and enhancing quality of life. These technologies drive efficiency and precision through automation and intelligent systems, reshaping sectors such as manufacturing, logistics, and agriculture. In healthcare, computational vision powers advanced diagnostic tools and surgical robotics, while robotics fosters personalized assistance and rehabilitation solutions. Additionally, the integration of these technologies in smart cities and innovative infrastructures offers smarter, safer, and more sustainable urban living environments.
R 10,638.69
Intelligent Technology for Power and Energy Systems
This book brings together breakthrough ideas and practical engineering insights, offering readers immediate access to innovative solutions that address today’s most pressing challenges in power and energy systems. It emphasizes the benefits of intelligent, data-driven approaches—especially those enabled by AI, IoT, and advanced smart grid technologies—that enhance reliability, efficiency, and resilience in modern energy infrastructures.Building on cutting-edge contributions from the symposium, the book introduces new perspectives on intelligent energy systems, energy internet and IoT applications, energy regeneration, storage technologies, and cybersecurity for critical infrastructure. By bridging theoretical research with real-world implementation, it demonstrates how intelligent technologies can transform the way energy is produced, distributed, and consumed, supporting global sustainability and decarbonization goals.With a broad interdisciplinary scope, the book is intended for researchers, engineers, industry professionals, and graduate students working in power engineering, intelligent systems, renewable energy integration, computer science, and applied sciences. Key uses include supporting academic research, guiding industrial innovation, informing policy decisions, and serving as a valuable resource for advanced teaching and professional development.
R 10,079.77
Intelligent Renewable Energy Systems
Focused on renewable energy systems and the development of information and communication technologies (ICTs) for their integration in smart grids, this book presents recent advances and methods that help to ensure that power generation from renewable sources remains stable, that power losses are minimized, and that the reliable functioning of these power generation units is maintained.The book highlights key topics and technologies for renewable energy systems including the intelligent control of power generators, power electronics that connect renewable power generation units to the grid, and fault diagnosis for power generators and power electronics. In particular, the following topics are addressed:• Modeling and control of power generators (PMSGs, DFIGs);• Modeling and control of power electronics (converters, inverters); • Modeling and fault diagnosis of the transmission and distribution Grid; and• Modelling and control of distributed power generation units (interconnected synchronous generators or photovoltaic units).Because of the above coverage, members of the wider engineering community will find that the nonlinear control and estimation methods presented provide essential insights into the functioning of renewable energy power systems, while the academic community will find the book a valuable textbook for undergraduate or graduate courses on renewable energy systems.
R 6,228.00
Pattern Recognition and Computer Vision
This 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18-20, 2024. The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.
R 3,557.84
Computational Intelligence in Engineering Science
This four-volume set constitutes the refereed proceedings of the First International Conference on on Computational Intelligence in Engineering Science, ICCIES 2025, in Ho Chi Minh City, Vietnam, during July 23-25, 2025. The 115 full papers presented in these proceedings were carefully reviewed and selected from 210 submissions. The papers are organized in the following topical sections: Part I: Machine Learning; Wireless Networks (6G) Part II: Computer Vision; Natural Language Processing Part III: Intelligent Systems; Internet of Things Part IV: Machine Learning; Control Systems
R 4,282.21
Machine Vision and Augmented Intelligence
This book comprises the proceedings of the International Conference on Machine Vision and Augmented Intelligence (MAI 2022). The conference proceedings encapsulate the best deliberations held during the conference. The diversity of participants in the event from academia, industry, and research reflects in the articles appearing in the book. The book encompasses all industrial and non-industrial applications. This book covers a wide range of topics such as modeling of disease transformation, epidemic forecast, image processing, and computer vision, augmented intelligence, soft computing, deep learning, image reconstruction, artificial intelligence in health care, brain-computer interface, cybersecurity, social network analysis, and natural language processing.
R 9,158.00
